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| license: apache-2.0 |
| language: |
| - en |
| metrics: |
| - accuracy |
| - precision |
| - f1 |
| library_name: sklearn |
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| |
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| 🚀 Use Cases |
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|
| | Use Case | Description | |
| |----------------------------------|-------------| |
| | 🔐 Password strength scoring | Quantitative scoring (0–10) for any given password | |
| | 🧠 Risk classification | Categorizes passwords as `Weak`, `Fairly Strong`, `Strong` | |
| | 🕵️ Threat emulation | Emulates password cracking heuristics to spot vulnerable patterns | |
| | 🧰 DevSecOps integration | Plug into CI/CD pipelines for password policy enforcement | |
| | 👨💻 User awareness tools | Build frontend UX tools to give users feedback on password creation | |
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| # 🔐 Trace.AI - AI-Powered Password Intelligence Engine |
|
|
| **Trace.AI** is an intelligent, ML-driven password checker designed to evaluate the **strength**, **structure**, and **policy compliance** of passwords. |
| Built for modern security infrastructures, it leverages machine learning to identify weak, predictable, or non-compliant passwords |
| based on real-world patterns and security datasets. |
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|
|
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| ## 🚀 Core Capabilities |
|
|
| ### ✅ Password Strength Classification |
| Trace.AI scores passwords as **Weak**, **Fairly Strong**, or **Strong** using a combination of rule-based feature extraction and machine learning. |
|
|
| ### 🎯 Pattern Recognition |
| Detects predictable and insecure patterns such as: |
| - Keyboard walks (`qwerty`, `asdf123`) |
| - Common substitutions (`p@ssw0rd`) |
| - Repeated sequences (`abcabc`, `123123`) |
| - Known dictionary or breached password similarities |
|
|
| ### 📏 Policy Compliance |
| Checks if passwords meet enterprise-grade security policies, including: |
| - Minimum length and entropy |
| - Required character types (upper/lowercase, digit, special) |
| - No whitespace, dictionary words, or reuse |
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| ## 📊 Datasets Used |
|
|
| Trace.AI was trained using curated, high-quality password datasets: |
|
|
| | Dataset | Description | |
| |--------|-------------| |
| | **cleanpasswordlist(modified)** | Real-world passwords list, modified and feature engineered for better prediction and scoring | |
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| ## 🧠 Machine Learning Models |
|
|
| Trace.AI supports and evaluates multiple ML models for robustness: |
|
|
| | Model | Strengths | Use | |
| |-------|-----------|-----| |
| | **RandomForest** | Non-linear classification, interpretable, fast | Production baseline | |
| | **XGBoost** | Handles imbalance, high accuracy, fast inference | Advanced detection | |
| | **Decision Trees** | Lightweight, interpretable | Edge device / fallback model | |
|
|
| All models are trained using engineered features like: |
| - Length, character diversity |
| - Entropy |
| - Keyboard patterns |
| - Regex-based leetspeak and substitution scoring |
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|
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| ## Project Goals |
|
|
| Trace.AI is engineered to support the following goals: |
|
|
| | Feature | Description | |
| |--------|-------------| |
| | 🔐 Password Strength Estimator | Predict if password is Weak, Moderate, or Strong | |
| | 🧠 Pattern Analyzer | Identify insecure sequences, leetspeak, keyboard walks | |
| | 📜 Policy Validator | Check adherence to defined password policies | |
| | 📤 Exportable Reports | Download prediction logs for security audits | |
| | 📈 Visual Dashboard | UI-based analysis of strength and structure (via Gradio) | |
|
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